Summary and Keywords

Human activities have rapidly accelerated global nitrogen (N) cycling since the late 19th century. This acceleration has manifold impacts on ecosystem N and carbon (C) cycles, and thus on emissions of the greenhouse gases nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4), which contribute to climate change.

First, elevated N use in agriculture leads to increased direct N2O emissions. Second, it leads to emissions of ammonia (NH3), nitric oxide (NO), and nitrogen dioxide (NO2) and leaching of nitrate (NO3−), which cause indirect N2O emissions from soils and waterbodies. Third, N use in agriculture may also cause changes in CO2 exchange (emission or uptake) in agricultural soils due to N fertilization (direct effect) and in non-agricultural soils due to atmospheric NHx (NH3+NH4) deposition (indirect effect). Fourth, NOx (NO+NO2) emissions from combustion processes and from fertilized soils lead to elevated NOy (NOx+ other oxidized N) deposition, further affecting CO2 exchange. As most (semi-) natural terrestrial ecosystems and aquatic ecosystems are N limited, human-induced atmospheric N deposition usually increases net primary production (NPP) and thus stimulates C sequestration. NOx emissions, however, also induce tropospheric ozone (O3) formation, and elevated O3 concentrations can lead to a reduction of NPP and plant C sequestration. The impacts of human N fixation on soil CH4 exchange are insignificant compared to the impacts on N2O and CO2 exchange (emissions or uptake). Ignoring shorter lived components and related feedbacks, the net impact of human N fixation on climate thus mainly depends on the magnitude of the cooling effect of CO2 uptake as compared to the magnitude of the warming effect of (direct and indirect) N2O emissions.

The estimated impact of human N fixation on N2O emission is 8.0 (7.0–9.0) Tg N2O-N yr−1, which is equal 1.02 (0.89–1.15) Pg CO2-C equivalents (eq) yr−1. The estimated CO2 uptake due to N inputs to terrestrial, freshwater, and marine ecosystems equals −0.75 (−0.56 to −0.97) Pg CO2-C eq yr−1. At present, the impact of human N fixation on increased CO2 sequestration thus largely (on average near 75%) compensates the stimulating effect on N2O emissions. In the long term, however, effects on ecosystem CO2 sequestration are likely to diminish due to growth limitations by other nutrients such as phosphorus. Furthermore, N-induced O3 exposure reduces CO2 uptake, causing a net C loss at 0.14 (0.07–0.21) Pg CO2-C eq yr−1. Consequently, human N fixation causes an overall increase in net greenhouse gas emissions from global ecosystems, which is estimated at 0.41 (−0.01–0.80) Pg CO2-C eq yr−1. Even when considering all uncertainties, it is likely that human N inputs lead to a net increase in global greenhouse gas emissions.

These estimates are based on most recent science and modeling approaches with respect to: (i) N inputs to various ecosystems, including NH3 and NOx emission estimates and related atmospheric N (NH3 and NOx) deposition and O3 exposure; (ii) N2O emissions in response to N inputs; and (iii) carbon exchange in responses to N inputs (C–N response) and O3 exposure (C–O3 response), focusing on the global scale. Apart from presenting the current knowledge, this article also gives an overview of changes in the estimates of those fluxes and C–N response factors over time, including debates on C–N responses in literature, the uncertainties in the various estimates, and the potential for improving them.

Acceleration of the Nitrogen Cycle

Nitrogen (N) is an essential nutrient for the growth and functioning of plants, animals, and humans. The Earth’s atmosphere consists for 78% of di-nitrogen (N2), but in this form N is unavailable to most living organisms. Instead, reactive N, which includes all forms of N except N2, is crucial for life on Earth. Reactive N occurs in inorganic reduced forms, such as ammonium (NH4+); in inorganic oxidized forms, such as nitrate (NO3−); and as both reduced and oxidized organic forms, such as amino acids. Since the late 19th century, human activities have rapidly accelerated the global N cycle and approximately doubled reactive N inputs to the environment (Galloway et al., 2004). This acceleration has mainly been driven by increases in N fertilizer use, the area cultivated with N2-fixing crops (Erisman, Sutton, Galloway, Klimont, & Winiwarter, 2008; Fowler et al., 2013; Smil, 2001) and fossil fuel combustion, to fulfill the food and energy demand of a growing world population (Galloway et al., 2008).

Impacts of the Nitrogen Cycle on Greenhouse Gas Emissions and Radiative Forcing

Figure 1. Linkages between Nr and greenhouse gas exchange discussed in this article. Red indicates a warming effect, blue indicates a cooling effect. Depicted processes are described in the text. NOx emissions are also associated with lightning, biomass and biofuel burning, and agricultural soils. NH3 results from biomass/biofuel burning, and increasingly NH3 is emitted from transport. For clarity these emissions are not displayed in Figure 1.

First, human N fixation increases N2O emissions, mainly by mineral and organic N-fertilizer use (Figure 1, top panel) but also from cultivating N2-fixing crops. Increased N fertilizer use in agriculture leads to additional N2O emissions from agricultural soils (denoted as direct N2O emission), but also from terrestrial and aquatic systems, including rivers, coastal zones, and open oceans, following volatilization or leaching of applied N and re-deposition and processing downwind or downstream of the agricultural regions where fertilizer is used (denoted as indirect N2O emissions) (Mosier et al., 1998; Voss et al., 2013). Other sources of anthropogenic N2O emissions include biomass burning, fossil fuel combustion, sewage, and industrial processes. The main natural sources of N2O emissions are tropical forest soils, coastal waters, and oceans (Syakila & Kroeze, 2011; Werner, Butterbach-Bahl, Haas, & Kiese, 2007).

Several studies have estimated the effect of human N fixation on net greenhouse gas exchange by comparing the cooling effect of CO2 uptake with the warming effect of (direct and indirect) N2O emissions. For Europe, De Vries et al. (2011a) estimated that CO2 uptake in agricultural, terrestrial, and marine ecosystems induced by N (NH3) emissions and N runoff from agriculture more than compensated N2O emissions from agricultural systems. However, they neglected the C sink-enhancing effects of NOx deposition and the C sink-reducing effects of NOx-induced formation of tropospheric O3 (e.g., Sitch, Cox, Collins, & Huntingford, 2007; Van Dingenen et al., 2009). Furthermore, in the long term the N-induced stimulation of the C sink may diminish as growth becomes increasingly limited by other nutrients such as phosphorus (Braun, Thomas, Quiring, & Flückiger, 2010; Peñuelas et al., 2013; Peñuelas, Sardans, Rivas-ubach, & Janssens, 2012).

Apart from the three mechanisms described above, there are several other ways through which N influences the Earth’s radiative forcing balance, including feedbacks through the formation of atmospheric inorganic aerosols, O3 and CH4. These feedbacks are not the focus of this review; however, they are briefly discussed at the end of this article.

Aim of This Article

This article discusses and quantifies impacts of human-induced N fixation on terrestrial and aquatic ecosystem N2O and CO2 exchange (emissions or uptake), in terms of net emissions expressed in CO2 equivalents. It gives an overview of the state of the science with respect to global-scale estimates for (i) N inputs and N fates, including NH3 and NOx emission estimates and related atmospheric N (NH3 and NOx) deposition and N-induced O3 exposure (Human Nitrogen Fixation, Nitrogen Fate, and Ozone Exposure), (ii) N2O emissions (Global-Scale Nitrous Oxide Emissions in Response to Nitrogen Inputs), (iii) ecosystem C–N responses and related increased C uptake due to N deposition, and (iv) ecosystem C–O3 responses and related decreased C uptake due to O3 exposure (Carbon Exchange in Response to Ozone Exposure. Global-scale estimates are presented based on the contemporary state of the science, using results from experimental studies, field measurements, and upscaling by modeling. We also present changes in estimates of those fluxes and C–N responses over time and discuss controversies and debates and notable discoveries or advances in time. The article ends with a discussion and outlook, including a summarizing overview of ranges in N2O and CO2 emission estimates in response to N inputs and O3 exposure and the total effect of human N fixation on net greenhouse gas exchange.

Human Nitrogen Fixation, Nitrogen Fate, and Ozone Exposure

Causes of Elevated Nitrogen and Ozone Exposure

Human nitrogen fixation

Human N fixation includes chemical N fixation in fertilizers, biological N fixation (BNF) by growing legumes, and NOx production/emissions due to fossil fuel combustion and biomass burning. Reactive N is naturally created from N2 by natural BNF, wildfires, and lightning. Natural BNF, excluding human-induced BNF by growing legumes, is by far the most important natural N source (Vitousek et al., 2002, 2013), accounting for more than 90% of non-anthropogenic N inputs to terrestrial ecosystems (Fowler et al., 2013; Galloway et al., 2004). In comparison, the amount of N fixed by lightning (mostly NOx), which becomes biologically available via atmospheric deposition, is relative small (Galloway et al., 2004), although highly important for atmospheric chemistry. Weathering of N contained in sedimentary rocks can also provide N locally (Dahlgren, 1994; Holloway et al., 1998; Morford, Houlton, & Dahlgren, 2011), although a more important role has been claimed as well (Holloway & Dahlgren, 2002).

Since the Industrial Revolution, agricultural and industrial activities have strongly increased reactive N creation. Before the 20th century, people tried to increase crop production either by enhancing BNF through the introduction of legumes (such as clover, beans, and soy) in crop rotations and pastures, or by using N-rich manure or guano. In 1908, however, Fritz Haber discovered a process to synthesize NH3 through chemical N2 fixation. By 1913, Carl Bosch had further developed Haber’s laboratory system for NH3 production at an industrial scale. About 40 years later, the combined discoveries of Haber and Bosch, known as the Haber-Bosch process, led to a strong increase in agricultural N fertilizer use, which became a main driver of crop yield increases (Erisman et al., 2008, 2013). The increased food production due to the Haber-Bosch process, combined with increased incomes, has also led to a change in diets globally, with more emphasis on meat consumption. Use of N for cultivation of feed crops has further caused an increase in the production of N in manure. Note that the related N production by manure is not N fixation as such, as it is recycled N, being taken up in feed by animals and excreted as manure. However, this recycling of N has largely increased N2O and NH3 emissions, thus affecting the greenhouse gas balance. Next to agriculture, reactive N is created as a by-product of industrial activities and combustion of fossil fuels leading to NOx emissions. Whereas NH3 emissions are mainly due to recycling of N, NOx emissions are part of the human N fixation, thus adding N to the system.

Ozone is a secondary pollutant that is formed in the presence of sunlight by the interaction of NOx with non-methane volatile organic carbons (NMVOC); carbon monoxide (CO); and, important on the global scale, CH4. Ozone is destroyed by photochemical reactions and enters the Earth’s surface via dry deposition. Dry deposition processes account for about 25% of the total O3 removed from the troposphere (Lelieveld & Dentener, 2000; Stevenson et al., 2006).

Tropospheric O3 pollution has shifted from a regional to a global issue because of its intercontinental transport (Derwent, Stevenson, Collins, & Johnson, 2004; Vingarzan, 2004). The increasing concentrations of tropospheric O3 are of concern, as O3 is not only the greenhouse gas with the third strongest radiative forcing (Forster et al., 2007; Myhre et al., 2013), but is also considered the most detrimental air pollutant for plants in Europe, the United States, and China in the early 21st century (Ashmore, 2005; Karnosky, Skelly, Percy, & Chappelka, 2007; Matyssek et al., 2007b; Ren et al., 2011), causing reduced forest and crop growth and C sequestration (Karnosky & Pregitzer, 2006; Paoletti, 2005; Tian et al., 2011; Wittig, Ainsworth, & Long, 2007). In addition, exposure to O3 causes a variety of health effects (REVIHAAP, 2013), which are not discussed in this article.

Global-Scale Estimates of Nitrogen Budgets for Terrestrial and Aquatic Ecosystems

Figure 2. Overview of (i) natural and human-induced N inputs by BNF, NOx emissions, and N fertilizer (Haber Bosch) inputs; and (ii) the fate of N due to N uptake by feed crops followed by production of N manure; N losses due to air emissions of NH3, NOx, and N2O; and N leaching and runoff to aquatic systems.

Nitrogen deposition mainly results from NOx emissions from combustion processes and NH3 emissions from agriculture. Table 1 presents quantitative estimates for global-scale N inputs to and losses from agricultural, terrestrial, and aquatic systems. These estimates are further discussed below. Nitrogen deposition estimates are discussed in more detail in the section on Global-Scale Nitrogen Deposition Estimates.

Table 1. Global-scale average natural (bold) and anthropogenic N input and N output estimates (Tg N yr−1) for agricultural, terrestrial, and marine systems according to various approaches. The estimates do not include uncertainties, but are based on consistency between inputs and outputs. The overall uncertainty in N outputs is likely within 50%.

Notes:
(1)
All data are based on Bouwman et al. (2013b) for the year 2000. The N input by manure is the amount excreted by animals and includes the N that is emitted from housing systems (10 Tg N yr−1). Comparable values for N inputs are given in Bouwman et al. (2013a), but these authors do not provide data for N leaching/runoff.

(2)
Based on Bouwman et al. (2013a). The N fixation is comparable to the most recent estimate of Vitousek et al. (2013). The sum of N deposition on agricultural systems and other terrestrial systems is comparable to results given by Dentener et al. (2006a).

(3)
Based on an N budget of the ocean by Voss et al. (2013). The value is includes a BNF of 140 Tg N yr−1 for the open ocean, in line with Fowler et al. (2013) and of 17 Tg N yr−1 for the coastal zones.

(4)
Based on Dentener et al. (2006a). Duce et al. (2008) assumed that additionally there is 30% not accounted for leading to 46./0.7 is ca 66 Tg N yr−1, but this value is likely an overestimate.

(5)
The total N input, being the sum of N fixation, N deposition, and N leaching/runoff is 253 Tg N yr−1.

(6)
Based on an N budget of the ocean by Voss et al. (2013). The N retention stands in this case for the N burial in sediments.

(10)
Estimated from the difference between N inputs and N outputs, being in the estimated range for N2-N emissions of 100–280 Tg N yr−1 given by Fowler et al. (2013).

(11)
Based on Voss et al. (2013). In quoting these authors, Fowler et al. (2013) gives a value of 80 Tg N yr−1 but seems too high. The N leaching/runoff is comparable to the output from agricultural systems and is input for marine/aquatic systems.

Global-scale nitrogen inputs to agricultural systems by fertilizer and manure

Various approaches have been used to assess global-scale N inputs in agriculture by fertilizer and manure (e.g., Bouwman et al., 2013b; Liu et al., 2010; Potter, Ramankutty, Bennett, & Donner, 2010). Estimates of N fertilizer inputs are mostly based on FAO data, while N manure inputs are based on FAO data on animal numbers combined with data on N excretion rates and manure use. Data presented in Table 1 are derived by Bouwman et al. (2013b), who estimated N inputs to agricultural land at 83 TgN yr−1 for synthetic fertilizer and 92 Tg N yr−1 for manure for the year 2000 (Table 1). Liu et al. (2010) estimated total N input to global croplands for the year 2000 at 137 Tg N yr−1, of which almost half is contributed by fertilizers. Fowler et al. (2013) estimated human N fixation for the year 2010 at 120 Tg N yr−1, of which 80% is used as N fertilizer (96 Tg N yr−1) and 20% as feedstock for industrial processes (24 Tg N yr−1). This range shows that these estimates are somewhat uncertain, but probably relatively minor compared to other terms in the N-budget.

Global-scale nitrogen inputs by biological nitrogen fixation

Biological N fixation (BNF) in agricultural soils can be considered an anthropogenic input as it is stimulated by cultivating N-fixing agricultural crops. Bouwman and colleagues (2013b) estimated agricultural BNF at 40 Tg N yr−1 based on land use data of N fixing crops. The uncertainty associated with this estimate is, however, likely at least 50%. Based on a compilation of measurements of BNF from a range of agricultural systems, Herridge, Peoples, and Boddey (2008) estimated global agricultural BNF at 60 Tg N yr−1, an estimate used by Fowler and colleagues (2013) in an overview of global N fluxes.

In terrestrial and marine systems, global-scale N inputs are dominated by natural BNF. Cleveland and colleagues (1999) proposed a global BNF estimate for terrestrial systems of 195 TgN yr−1, with a range of 100–290 Tg N yr−1, by deriving average BNF rates for different biomes from empirical studies and multiplying this rate with the area of each biome. A lower estimate of 128 Tg N yr−1 was used by Cleveland et al. (2013), divided in 23 Tg N yr−1 by free-living microorganisms, based on upscaling biome averages (Bai, Houlton, & Wang, 2012), and 105 Tg N yr−1 by symbiotic N fixation. The latter value was based on a biogeochemical process model approach calculating the required N fixation for storing additional C in plants and soils after 1900 (Wang & Houlton, 2009). Recently, Vitousek, Menge, Reed, and Cleveland (2013) calculated terrestrial BNF for the preindustrial N cycle as the difference between estimated N inputs (except BNF) and N losses, while assuming a steady state. Nitrogen inputs included lightning and atmospheric deposition of N transported from oceans to land, while N losses included air emissions, runoff, and N leaching from land to oceans. Their approach yielded a much lower value of 58 Tg N yr−1. This approach was also applied by Bouwman et al. (2013a), who suggested a value of 53 Tg N yr−1 (Table 1). Estimates for BNF in marine systems, for example, by Galloway et al. (2004), are also based on upscaling measured BNF rates, focusing on N fixation by the Trichodesmium species. Their estimate of 120 Tg N yr−1 was recently updated by Voss et al. (2013) to 157 Tg N yr−1 (Table 1), but the uncertainty is probably larger than suggested by a range of 120–157 Tg N yr−1.

Global-scale nitrogen emissions

In all approaches, global-scale N (NH3, NOx and N2O) emission estimates are made by multiplying activity data with emission factors, but the activities included and the emission factors applied vary among the estimates. Global-scale NH3 and NOx emissions are discussed below, while global-scale N2O emissions are discussed in detail in the section on Global-Scale Nitrous Oxide Emissions in Response to Nitrogen Inputs.

Global-scale NH3 emissions are dominated by anthropogenic emissions from agriculture (approximately 90%). Dentener and Crutzen (1994) were the first to derive a global spatially resolved NH3 emission inventory, followed by higher resolution inventories by Bouwman et al. (1997) and Beusen, Bouwman, Heuberger, Van Drecht, and Van der Hoek (2008). The latter approaches are used in the IMAGE-N model (Bouwman et al., 2013b). In this approach, NH3 volatilization from excreted manure is calculated based on emission factors for 10 animal categories, based on Bouwman et al. (1997), while NH3 volatilization from fertilizer and animal manure application is calculated with an empirical regression model based on factors related to (i) agricultural management, including crop type, fertilizer type, and fertilizer application technique; and (ii) environmental conditions, including climate, soil pH, and soil cation exchange capacity. Using this approach, global NH3 emissions from agriculture have been estimated at 34 Tg N yr−1 for the year 2000 (Table 1). Other estimates include those based on the EDGAR database (Van Aardenne et al., 2001). A recent overview of global NH3 emission estimates has been given by Sutton and colleagues (2013), who estimated total global NH3 emissions at 59 Tg N yr−1 for the year 2000, based on recent estimates with EDGAR4.1 (EC-JRC & PBL, 2011), and adding ~8 Tg N yr−1 from oceans and ~12 Tg N yr−1 from humans, waste, pets, wild animals and natural soils from other sources (Bouwman et al., 1997; Dentener & Crutzen, 1994; Van Aardenne et al., 2001).

Global-scale nitrogen losses through leaching and runoff

Very few models assess global-scale N leaching and runoff to ground- and surface water. One example is the IMAGE-N model, which estimates losses by leaching and runoff at 39 Tg N yr−1 (Bouwman et al., 2013b, Table 1), based on an approach described in detail in Van Drecht and colleagues (2003). This means that the fraction of global N inputs to agricultural systems (248 Tg N yr−1) lost through leaching and runoff is 0.16. At European scale, the average leaching/runoff fraction estimated by four different models, including the IMAGE-N, varied from 0.11–0.26, with IMAGE-N estimating value of 0.23 (De Vries et al., 2011b). This is lower than the leaching/runoff fraction of 0.3 proposed by the 2006 IPCC guidelines (IPCC, 2006). The IPCC value, however, is most likely too high for large-scale applications, although it may hold in cases of excessive N inputs.

Global-Scale Nitrogen Deposition Estimates

Observations and Model Approaches

Continuous large-scale observations of wet deposition have been made since the 1970s in North America and Europe, and since the 2000s in parts of Asia, Africa, and South America (Vet et al., 2014). Observation-based dry deposition estimates involve simultaneous measurements of N components and micro-meteorological parameters, and are scarcely available. Therefore, regional and global atmospheric chemistry-transport model calculations are the main source of information on total (wet + dry) deposition of N (NH3, NOy) on continental to global scales (Dentener et al., 2006b; Dentener et al., 2014). Global models describe the emissions, chemical transformations, transport, and removal by wet and dry depositions on typical grid resolutions between 1°×1° and 4°×5°, whereas regional models are typically run on a resolution of 10 km×10 km.

Figure 4. Global-scale TM5 model estimates of (a) wet deposition of NOy and NHx, (observations are displayed on top of model results) and (b) dry deposition of NOy and NHx for the year 2000.

While the HTAP models have not been specifically designed to accurately calculate total deposition, comparison of model results with wet deposition observations in Europe and North America showed an acceptable agreement. The agreement in other parts of the world (East Asia, South Asia, Africa, South America) is less satisfactory, due to uncertainties in emission inventories and model accuracy. This is also due to limitations in spatial representation and quality of measurements (see Figure 4), thus lacking strong constraints on the model results.

Uncertainties in global-scale N deposition estimates

Table 2 presents an overview of global estimates for N deposition on various ecosystems from four single- and multi-model evaluations from recent decades. The early estimate of N deposition by Holland and colleagues (1997, 1999) is markedly lower than later estimates. This upward revision reflects changing insights in the magnitude and temporal changes of emissions, and very likely also different definitions of forest land-cover in the earlier studies, which affects the terrestrial N deposition estimates. The three later studies, using the same generation of models and emissions representative for the beginning of the 2000s, give estimates for global N deposition of around 104 ±14 Tg N yr−1, with 60 ± 6 Tg N yr−1 over land, of which approximately 23 Tg N yr−1 on forests, that is, near 13 Tg N yr−1 NHx deposition and near 10 Tg N yr−1 NOy deposition (Dentener et al., 2006b). The latter number is in good correspondence with Lamarque et al. (2005). The real uncertainty linked to N deposition estimates is likely much higher than the model spread alone suggests, as models are partly based on the same emission datasets, and it is difficult to verify these budgets in large parts of the world.

Table 2. Overview of estimates for total N deposition on land, on forest, and total global N deposition. Note that Dentener et al. (2006b), Vet et al. (2014), and Lamarque et al. (2013) are representative for the early 2000s, whereas Holland et al. (1997, 1999) emission estimates are for the early 1990s. Numbers between brackets refer to Holland et al. (1999) based on the MOGUNTIA model.

Global-Scale Ozone Exposure Estimates

Observations and Model Approaches

Surface O3 is not only a powerful greenhouse gas, but also directly affects human health and the productivity of crops and natural vegetation, including forests. Consequently, O3 is a regulated component. In Europe, for example, the limit value of 60 parts per billion (ppb) is not to be exceeded on more than 25 days per year. Therefore, hundreds of air quality stations in Europe and the United States routinely monitor O3 concentrations. These stations, however, are typically located in the vicinity of urban agglomerations, with limited representativeness for agricultural and (semi-)natural ecosystems. Routine O3 observations over ecosystems are less frequent. In Europe, the EMEP rural network provides ca. 130 monitoring stations (Tørseth et al., 2012), and in the United States, CastNet and the National Park Service provide ca. 60 monitoring stations (Cooper et al., 2013). In other regions, quality-controlled, long-term O3 observations are insufficient to provide information on large-scale O3 concentrations. Therefore, models are the main source of information on vegetation exposure to O3 at the global scale.

Modeling indicators for ozone impacts on plant growth

Large-scale O3 impacts on vegetation have until recently been estimated by threshold-based indicators like AOT40 (growing season accumulated O3 exposure over a threshold of 40 ppb expressed in ppm hour). Such threshold values are derived from field experiments that correlate observed O3 concentrations with vegetation damage. Target values in EU policy making for AOT40 are 3 ppm hour for protection of crops, and 5 ppm hour for forests. AOT and similar indicators have the advantage that they can be relatively easily estimated from atmospheric observations as well as model calculations. Van Dingenen and colleagues (2009), for example, used model-calculated AOT to estimate global impacts of present and future O3 on crop production. However, a drawback of these indicators is that they do not account for biophysical processes that may limit O3 damage; for example, under drought stress, plant stomata may be closed and O3 uptake limited. Recent research focused on relating measurements of O3 flux into vegetation and O3 damage (e.g., Mills et al., 2007). Phytotoxic Ozone Doses (PODs) over certain threshold values were found to be a more reliable indicator for vegetation damage than AOT40 (see Carbon Exchange in Response to Ozone Exposure). Based on a limited set of Free-Air Carbon dioxide Enrichment (FACE) experiments, Sitch and colleagues (2007) used model-computed O3 fluxes into natural vegetation to estimate O3-C cycle interactions.

Spatial and temporal variation in ozone exposure at global scale

Concentrations of tropospheric (column) O3, and surface O3 in particular, are strongly co-determined by vegetation uptake. Ozone production and vegetation activity co-vary seasonally. Figure 5 shows the resulting seasonal variations of O3 concentrations and O3 deposition fluxes, as well as AOT40 in the growing season over the world.

In winter, O3 concentrations are mostly below 30 ppb, except for Central Africa where concentrations up to 75 ppb occur in the winter period (December–February), related to biomass burning in that region (Figure 5a). Summer O3 concentrations, however, reach 40–60 ppb over large parts of the Northern Hemisphere (Figure 5b), while O3 dry deposition varies strongly between seasons, from 1×10−10 kg m−2 s−1 in the Northern Hemisphere winter to more than 5×10−10 kg m−2 s−1 in summer (Figure 5c and 5d). The higher dry deposition levels in summer are caused by higher O3 concentrations (by a factor 2–3), but most importantly by the stronger vegetation uptake in summer. In line with the summer O3 concentrations, AOT40 values calculated for the summer period are much lower in the Northern than in Southern Europe, where O3 concentrations more often exceed the 40 ppb threshold. Again, the highest values are calculated for central Africa.

Uncertainties in global-scale ozone exposure

A recent global model comparison of O3 deposition (Hardacre, Wild, & Emberson, 2015) indicates relatively large discrepancies between model results and dry deposition flux measurements in the United States and Europe, with differences up to a factor of two, but no clear systematic bias. The study suggests that some difference may be attributable to inconsistent land-cover classifications and outdated dry deposition routines included in the atmospheric models.

Nitrous Oxide Emissions in Response to Nitrogen Inputs

Sources, Processes, and Factors Affecting Nitrous Oxide Emissions

Sources of nitrous oxide

Joseph Priestley’s discovery of nitrous oxide (N2O), commonly known as laughing gas, occurred in 1772. However, it was not until 1938 that researchers discovered it is also present in the Earth’s atmosphere. Systematic measurements of atmospheric N2O concentrations started in the 1960s (Badr & Robert, 1992). Sources of atmospheric N2O have been attributed from the beginning to microbial N turnover processes in soils and sediments, specifically denitrification, as well as photo-chemical processes in the atmosphere (e.g., Bates & Hays, 1967). This view remains largely unchallenged, though current budgets include a few additional sources of N2O, such as combustion processes, biomass burning, or nylon production (Fowler et al., 2009). Furthermore, microbial processes in open oceans and in coastal seas have been confirmed as a major source for atmospheric N2O (Bange, 2006; Syakila & Kroeze, 2011).

At present, around 60% of anthropogenic N2O emissions are directly linked to food production, caused by the use of synthetic and organic fertilizers or animal waste management (Syakila & Kroeze, 2011). Moreover, atmospheric N deposition has been identified as a major driver of increased N2O emissions from terrestrial ecosystems (Butterbach-Bahl et al., 2011a). This holds specifically for N deposition to forests, which ranges from less than 5 kg N ha−1 yr−1 in Northern Europe to more than 60 kg N ha−1 yr−1 in Central and Western Europe and China (Dise, Rothwell, Gauci, Van der Salm, & De Vries, 2009; Liu et al., 2013). Deposition levels on shrubland and grassland are about half as high, that is, 3–30 kg N ha−1 yr−1. Atmospheric N deposition increases N availability in soils, thereby decreasing competition for N between plants and microbes in the rhizosphere, resulting in more N2O emissions (Schimel & Bennett, 2004).

Mechanisms causing nitrous oxide production in response to nitrogen inputs

N2O is produced during a series of oxidative and/or reductive microbial processes (Butterbach-Bahl et al., 2013). The most important processes are nitrification, that is, the oxidation of reduced NH4+/NH3 to NO3−, and the reductive process of denitrification, which uses NO3−/NO2−/NO or even N2O as receptors for electrons to produce N2 and to oxidize carbon substrates. Some microbial organisms combine oxidative and reductive processes, for example, nitrifier-denitrification, a process that might contribute significantly to N2O production in some soils (e.g., Kool et al., 2010). Generally, linking soil N2O emissions to individual processes or microbial communities is still associated with significant uncertainty, even though it is generally assumed that microbial denitrification is the most dominant process at regional to global scales (Butterbach-Bahl et al., 2013). Moreover, it is often overlooked that a large share of N2O produced in soils is also consumed there, mostly as a substrate for denitrification to form N2 (Clough, Sherlock, & Rolston, 2005; Yano et al., 2014).

Microorganisms capable of producing N2O via nitrification and denitrification processes belong to archaea, bacteria, and fungi (Hayatsu, Tago, & Saito, 2008). Next to microbial processes, N2O is also produced by chemical degradation of intermediate inorganic N compounds of microbial processes or of N compounds originating from atmospheric N deposition such as NO2−/NO3− or NH2OH (hydroxylamine) (Butterbach-Bahl et al., 2013).

N2O emissions from soils are controlled by a number of environmental factors (Butterbach-Bahl et al., 2013), such as (i) substrate availability, which in the case of denitrification is not only N-oxides but also easily degradable C substrates, as denitrification is a heterotrophic process; (ii) microbial community composition, which is affected by soil management, environmental conditions, and vegetation; (iii) soil moisture conditions as affected by soil texture and climate, with high moisture and high temperature favoring high N2O emissions due to effects on aeration (or increased anaerobia, which favors denitrification) and microbial activity, though peak emissions can also be observed during freeze-thaw events (De Bruijn, Butterbach-Bahl, Blagodatsky, & Grote, 2009); and (iv) soil pH, which influences the denitrification efficiency and the ratio of N2:N2O during denitrification, which both increase with an increase in pH (Bakken, Bergaust, Liu, & Frostegård, 2012).

These factors in turn are all affected by management, especially substrate availability via fertilization, soil pH via liming, and soil aeration via tillage (Li et al., 2005). Emission factors used by the IPCC assume a linear relationship between N2O emissions and N inputs (1% is emitted as N2O). A large collection of N2O emission measurements from agricultural fields showed, however, that N2O emissions are nonlinearly related to the N application rate and further affected by crop type, climate, and soil factors (Bouwman, Boumans, & Batjes, 2002; Stehfest & Bouwman, 2006). A recent meta-analysis also shows that N2O emissions do not respond linearly, but exponentially to N inputs, that is, with no or little change at low N application or deposition rates, but high losses if ecosystems get saturated with N and N availability strongly exceeds plant N demand (Shcherbak, Miller, & Robertson, 2014; Van Groenigen, Velthof, Oenema, Van Groenigen, & Van Kessel, 2010).

Loss rates of N2O from forest soils as a percentage of total atmospheric N input have been estimated between 3 and 5%, that is, several times higher than the globally used IPCC Tier 1 emission factor of 1% (Van der Gon & Bleeker, 2005) see the section on Approaches to Quantify Large-Scale Nitrous Oxide Emissions. This strong response of N2O emissions to atmospheric N deposition to natural systems might not only be driven by increased N availability, but also by soil acidification induced by sulfur and N deposition. Decreases in forest soil pH have been widely observed in regions with high atmospheric N deposition in Europe and the United States (e.g., Warby, Johnson, & Driscoll, 2009; Yang et al., 2015) and especially in China where both N and S deposition levels are still high (Du et al., 2014, 2015; Yang et al., 2015) and where forest soils are sensitive to acidification in large parts of the country (Hicks et al., 2008). These decreases in pH favor the emission of N2O, as they increase the product ratio of N2O:N2 during denitrification (Bakken et al., 2012; Kesik, Blagodatsky, Papen, & Butterbach-Bahl, 2006).

Atmospheric N deposition also leads to a decrease of the C:N ratio of soils. Recent analyses have shown that with a decrease of the C:N ratio below a threshold value of approximately 20:1, N2O emissions from forest soils might increase exponentially (Butterbach-Bahl & Dannenmann, 2012). Moreover, as a consequence of chronic N deposition, forest ecosystems might become N-saturated (Aber et al., 1989) and leach significant amounts of NO3− to ground- and surface water. This leached N can eventually also become be a source for atmospheric N2O (Well & Butterbach-Bahl, 2010).

Below, the research approaches are presented that have been used to quantify impacts of N inputs on N2O emissions, including emission factor approaches, empirical models, and process-based models, followed by an overview of global-scale N2O emission estimates based on those approaches.

Approaches to Quantify Large-Scale Nitrous Oxide Emissions

A combination of laboratory studies and field measurements is used to gain insight in and assess N2O emissions in response to N inputs. Scaling of laboratory findings to the field situation remain difficult if those are not constrained and accompanied by long-term field studies targeting specific research questions, for example, effects of changes in environmental conditions or management on N2O fluxes. In addition, both empirical and process-based modeling approaches are needed for upscaling results from those studies from plot to regional scales, as discussed below. The discussion below focuses on the quantification of impact of N inputs on direct N2O emissions from terrestrial systems, while the impact on N2O emissions from freshwater and marine systems is included by the use of indirect emission factors, accounting for N leached from soils to groundwater and surface water.

Emission factor approaches

N2O emission estimates at the global scale are often based on the IPCC emission factor approach, which assumes a fixed percentage of N inputs is converted to N2O (e.g., Fowler et al., 2009; Syakila & Kroeze, 2011). In the IPCC Tier 1 approach (IPCC, 2006), default N2O-N emission factors are used to estimate direct N2O emission from fertilizer application (1%), animal manure storage (1%) and grazing systems (2%), as well as indirect N2O emissions from NH3 emissions (1%) and from N leached from soils to groundwater and surface water (0.75%). This approach has been critically discussed, especially as the IPCC emission factor of 1% for N fertilizer might not reflect the observed changes in historic atmospheric N2O concentration (Crutzen, Mosier, Smith, & Winiwarter, 2008; Davidson, 2009). Davidson (2009) suggested emission factors of 2.0% for N2O emissions from manure N and 2.5% for N2O emissions from synthetic fertilizer N application. These values also integrate indirect N2O emissions following N transport and processing downwind and downstream of the site of application or creation. However, emission factors are only valid at larger scales, that is, national to global scales, since they represent statistical means and do not consider, for example, effects of site- or region-specific soil management, soil properties, or climate on emissions (Del Grosso, Wirth, Ogle, & Parton, 2008b). Taken together an emission factor of 3.8–5.1% relating to all “new” reactive N created seems to best align with atmospheric N2O concentration increases during the last century or so (Reay et al., 2012).

Empirical relationships

Since Tier 1 emission factor approaches such as used by the IPCC or Davidson (2009) do not provide reliable estimates of N2O emissions at regional and site scales, many models are based on empirical relationships between observed soil N2O fluxes and a number of environmental parameters, such as soil properties, climate, and management. The most cited example is a global meta-analysis of N2O emissions and governing environmental parameters used in the global model IMAGE. In IMAGE, N2O emissions from fertilizer and manure application are calculated with an empirical regression model based on (i) management factors (N application rate per fertilizer type, type of crop), and (ii) environmental factors (climate, soil organic C content, soil texture, drainage, and soil pH). The empirical relationships are based on a large collection of N2O-N emission measurements (near 1000) from agricultural fields, as reported in Bouwman et al. (2002) and Stehfest and Bouwman (2006). Philibert, Loyce, and Makowski (2012) estimated and then investigated the uncertainty of the IPCC Tier 1 default N2O-N emission factor of 1% by fitting 13 different models to this published dataset, including exponential and linear functions. They concluded that the exponential models outperformed the linear models, implying that the emission factor increases as a function of applied N.

Process-based models

Process-based models are increasingly used to assess N2O emissions at regional to global scales. Experimental studies are the basis for the development and testing of such models, which can then be used to scale up findings from experimental plots in space and time, for example, for inventory purposes, climate change impact studies, or the development of mitigation strategies. Examples include LandscapeDNDC (Haas et al., 2013), DayCENT (e.g., Del Grosso, Halvorson, & Parton, 2008a), or ECOSYS (Grant & Pattey, 1999). These models simulate N2O emissions based on the underlying physio-chemical processes and consider effects of variations in environmental factors on emissions by simulating key microbial processes such as mineralization, nitrification, or denitrification as well as site hydrology or plant growth and nutrient uptake dynamics. For regional application, models are linked to spatially explicit information on soils, management, climate, etc. As for the empirical approach realized within, for example, IMAGE, the consideration of region-specific information allows the identification of regional hotspots or temporal dynamics of emissions in response to changes in climate or management. Both empirical models and process-based biochemical models are also used to identify feasible options to mitigate emissions, for example, by adaptation of crop management practices. For example, Haas et al. (2013) estimated regional soil N2O emissions for the state of Saxony using both a process-based model and the IPCC emission factor approach, and showed that both approaches give similar results for total N2O emissions. The process-based model, however, revealed likely emission hotspots originating from soil properties and climate, and not only due to N fertilizer use. Similar results have also been obtained by using process-based models for national and global greenhouse gas accounting (Del Grosso et al., 2009; Del Grosso, Parton, & Mosier, 2006).

Anthropogenic N2O emissions are mainly caused by global mineral and organic N fertilizer use (59%) followed by fossil fuel combustion(18%), biomass burning (10%), atmospheric deposition (9%), and human sewage (3%) (IPCC, 2007). An overview of global-scale N2O emission estimates according to two different IPCC approaches (IPCC, 1997, 2006) as reported in Syakila and Kroeze (2011) and FAO (2013), the IMAGE model (Bouwman et al., 2013b), the Edgar database (EC-JRC & PBL, 2011) and a simple emission factor model by Davidson (2009) is given in Table 3. All estimates include emissions from agriculture, in most cases from various agricultural sources, and biomass burning while four studies also include industry, energy and transport, and/or human waste, allowing estimates for the total global-scale N2O emission (Table 3).

Notes: “NR” = Not reported, but included in the calculations. “–”= Not estimated, “NA” = Not applicable.

(1)
For the year 2000 it was 7.2 Tg N2O-N yr−1.

(2)
For the year 2000 it was 5.9 Tg N2O-N yr−1.

(3)
Only includes savannah burning and crop residue burning.

(4)
Total N2O emissions from agriculture reported in Bouwman et al. (2013a) are 6.4 Tg N2O-N yr−1. The subdivision in 4.8 Tg N2O-N yr−1 for total direct emissions from agricultural soils and 1.6 Tg N2O-N yr−1 for animal waste management is based on Bouwman (pers. comm.). In IMAGE, N2O emissions due to inputs by N fertilizer, N manure, and N fixation by legumes are also calculated separately, but these are not stored nor reported.

(12)
Includes direct and indirect emissions from manure application and manure handling only.

(13)
Emissions from agricultural soils are calculated as residual terms and thus include both direct and indirect emissions.

(14)
Includes N2O from Nylon production and from (mobile and stationary) fossil fuel combustion.

The IPCC-based approaches and the EDGAR database include separate estimates for N2O emissions due to application of N fertilizer and N manure, N fixation by legumes, N input by crop residues, N mineralization from cultivated histosols, and N manure storage and grazing. In the other two approaches, source specific estimates are either not calculated or reported (see Table 3). Indirect emissions due to agriculture-induced atmospheric N deposition on land, N leaching/runoff, human N in sewage, and atmospheric N deposition on ocean are quantified separately by Syakila and Kroeze (2011), while the IMAGE model and the EDGAR database only indirect emissions due to agriculture-induced land N deposition and N leaching/runoff. Figure 6 shows spatial patterns in N2O emissions for the year 2000 calculated by the IMAGE model.

Figure 6. Spatial patterns in anthropogenic N2O emissions for the year 2000 calculated with the IMAGE model

(Source: IMAGE results, Bouwman, pers. comm.).

Results show that estimates for direct N2O emissions from agricultural soils are rather comparable for all methods (1.6–2.4 Tg N2O-N yr−1) except for the IMAGE model (Bouwman et al., 2013b) being more than double as high (Table 3). Syakila and Kroeze (2011) estimated total agricultural N2O emissions for the year 2006 at 7.1 and 5.3 Tg N2O-N yr−1, respectively, when using the revised 1996 IPCC Guidelines (IPCC, 1997; Mosier et al., 1998) and the more recent 2006 IPCC Guidelines (De Klein et al., 2007; IPCC, 2006). Considering the likely increase in global N2O emissions between 2000 and 2006, the estimate by Syakila and Kroeze (2011) obtained with the 2006 IPCC Guidelines is nearly equal to the estimate by Davidson (2009) and also to the estimate of Crutzen et al. (2007) of 4.3–5.8 Tg N yr−1 (latter not shown in Table 3). The Syakila and Kroeze (2011) estimate for 2006 (5.3 Tg) is, however, 55% higher than the Edgar 4.2 estimate (3.4 Tg). N2O emission estimates from the IMAGE model (Bouwman et al., 2013b) are approximately 50% higher than those obtained by all other methods, mainly due to much higher direct emissions from agricultural soils (Table 3).

The uncertainty in indirect emissions is even much higher, especially due to uncertainties in N leaching/runoff-induced N2O emissions. The approach using the N2O emission factors based on the IPCC 1997 Tier 1 approach leads to the highest, but those factors have been found to be too high and have been downscaled accordingly. However, even the more recent estimates show a variation of a factor 2 (Table 3).

Furthermore, the potential contribution of deep oceans to emit N2O in response to human-induced N deposition is not included in most approaches, except for Syakila and Kroeze (2011). The most recent estimates for agricultural N2O emission are presented in Tubiello et al. (2013) based on FAOSTAT activity data on livestock, fertilizer use, crop harvest, etc., and IPCC Tier 1 default emission factors according to the IPCC 2006 approach (FAO, 2013). The database provides a complete and coherent time series of emission estimates from 1990–2010 at country level (Tubiello et al., 2013). These estimates are lower than all previous estimates and equal 4.5 Tg N2O-N yr−1 for 2010 and 3.7 Tg N2O-N yr−1 for the year 2000 (presented in Table 3 for intercomparison). The increase in N2O emissions between 2000 and 2010 is mainly driven by an increased N fertilizer use (Tubiello et al., 2013).

Non-agricultural N2O emissions due to biomass burning, industry and transport, and human waste are not reported consistently, apart from the EDGAR database, but in the latter biomass burning is limited to agriculture, thus likely being an underestimate. Considering the various sources, and the neglect of human waste and atmospheric N deposition on ocean in most of the estimates, the range in total anthropogenic N2O emissions varies most likely between 7.0 and 9.0 Tg N2O-N yr−1.

Carbon Exchange in Response to Nitrogen Inputs

Impacts of Nitrogen on Plant Growth and Carbon Exchange

As only a small proportion of the Earth’s biota can convert inert N2 to reactive N by biological N fixation (BNF), N limitation is widespread in terrestrial and marine ecosystems (LeBauer & Treseder, 2008; Vitousek & Howarth, 1991). Consequently, external N inputs via N deposition can stimulate NPP and thus lead to additional C sequestration via biomass accumulation. Increased N deposition can also increase C sequestration in the soil due to increased soil C inputs and/or reduced decomposition of organic matter (Janssens et al., 2010). The importance of enhanced past and future N deposition for global C sequestration has been a research topic for decades. Insights into the effects of external N inputs on the global C cycle are crucial because profound changes in global N deposition are expected in the coming decades (Fowler et al., 2013; Gruber & Galloway, 2008).

Elevated N inputs to agricultural soils may also cause C sequestration by increasing crop production and thereby C input to soils in the form of crop residues. This effect, however, is likely to contribute much less to N-induced C sequestration at the global scale than forests (Liu & Greaver, 2009; see also the section on Global-Scale Estimates of Carbon Exchange in Response to Nitrogen Inputs). Nitrogen deposition also affects the net CO2 exchange from marine systems in oceanic areas that are either perennially or seasonally depleted in surface nitrate. In these oceanic areas, it is likely that N has a critical role in controlling primary production and one may assume that all N that is deposited is ultimately taken up by unicellular algae that dominate the living biomass in the ocean (Duce et al., 2008). N-induced primary production is expected to increase C storage in the ocean either via the biological pump, whereby CO2 in the upper ocean is fixed by primary producers and transported to the deep ocean or via the microbial carbon pump, which drives an accumulation of recalcitrant dissolved organic matter (Jiao et al., 2010).

The impact of N deposition on C sequestration is determined by the magnitude and spatial extent of N deposition (see the section on Global-Scale Nitrogen Deposition Estimates) and the C response to N deposition (C–N response). Below, the research approaches are presented that have been used to quantify impacts of N deposition on forest productivity and forest C sequestration, as forests are the most important terrestrial C sink, followed by a simple approach to assess the impacts of N on global-scale C sequestration in all terrestrial freshwater and marine ecosystems.

Stoichiometric scaling

Stoichiometric scaling is a straightforward approach to assess C–N responses of forest ecosystems. The approach is based on the observation that ratios of C:N:P are relatively constant, mirroring the metabolic demand of an average living cell (Deutsch, Sarmiento, Sigman, Gruber, & Dunne, 2007; Klausmeier, Litchman, Daufresne, & Levin, 2004). The effect of N deposition on C sequestration can thus be calculated by multiplying (i) the fractions of external N inputs that are retained in the forest ecosystem, with (ii) the allocation of the retained N to different forest ecosystem compartments (woody tissue, and non-woody tissues and soil), and (iii) the C:N ratio of each compartment (De Vries, Du, & Butterbach-Bahl, 2014). Nitrogen allocation to woody tissue (stems and coarse roots) with long turnover times (decades to centuries) and high C:N ratios (200:1–500:1) leads to a larger N-induced C sequestration than N allocation to non-woody tissues (e.g., leaves and fine roots) with a shorter turnover time (month to years) and lower C:N ratios (30:1–80:1). Carbon sequestration is thus dominated by the retention in woody biomass and soil.

Experimental nitrogen addition studies

Experimental N addition studies measure parameters related to plant and soil C sequestration in control plots and experimental plots with different N treatments. These studies can directly estimate the C–N response, but their results are only valid for the specific location where the experiment has been performed, which limits regional and global application (Sutton et al., 2008). Insight in the representativeness of these studies can best be obtained from meta-analysis of a large number of N addition experiments, including C responses of woody and non-woody compartments and soil. Such meta-analyses of data from N addition experiments on carbon responses, such as NPP and net ecosystem CO2 exchange, have been performed (e.g., LeBauer & Treseder, 2008; Liu & Greaver, 2009; Wamelink et al., 2009), but results are mostly presented as a percentage change in carbon response and not as C–N response.

Field-based monitoring studies across nitrogen deposition gradients

An indication of C–N responses can also be derived from field-based monitoring across N deposition gradients, but this approach requires a careful accounting for the influence of other drivers such as age of the forest or plantation, climate, and atmospheric CO2 concentrations.

Magnani et al. (2007) argued that N deposition is the main driver of C sequestration in temperate and boreal forests in Europe and North America. They estimated that for each additional kg of wet N deposition, forests will take up an additional 700 kg C (a C–N response of 700). This has aroused an intense scientific debate thereafter. Sutton et al. (2008) analyzed the effects of total N deposition on net ecosystem productivity (NEP) in 22 European forests and lowered the C–N response to 149 kg C per kg N. The C–N response was further reduced to 50–75 kg C per kg N after excluding the contribution of climatic factors. When accounting for the impacts of other drivers, growth observations at more than 350 long-term monitoring plots in Europe indicates a C–N response of 19–26 kg C per kg N (Laubhann, Sterba, Reinds, & De Vries, 2009; Solberg et al., 2009). Based on the latter two field studies, considering all the main factors affecting forest growth, De Vries et al. (2008) concluded that N deposition on European forests leads to an additional C sequestration in biomass of 20–40 kg C per kg N.

These estimates are in line with estimates of N addition experiments and stoichiometric scaling presented above. A comparable range, that is, 20–30 kg C per kg N, was found in an evaluation of a global data set of 80 forest sites, combining eddy covariance measurements of net carbon exchange with modeled N deposition, environmental variables, and stand characteristics (Fleischer et al., 2013).

Global carbon–nitrogen cycle models

Currently, the main research approach to assess the impacts of N inputs on global C sequestration is process-based models that simulate global C and N cycles and their interactions. These models have originally been developed to assess changes in ecosystem structure and C sequestration in response to shifts in climate and CO2 concentration on a global scale (e.g., Cramer et al., 2001, 2004). Many of these early C-cycle models did not include nutrient limitations for ecosystem C accumulation, and were therefore criticized for exaggerating the terrestrial biosphere’s potential to absorb CO2 (Hungate, Dukes, Shaw, Luo, & Field, 2003). Similarly, these models did not include impacts of O3 exposure on tree growth (see the section on Carbon Exchange in Response to Ozone Exposure).

Global-Scale Estimates of Carbon Exchange in Response to Nitrogen Inputs

Approach and quantification

In order to quantify the impact of global N inputs on CO2 exchange we distinguished between agricultural systems, forests, other (semi-) natural vegetation and marine systems, thus neglecting freshwater ecosystems. An indicative estimate of the N impact on C sequestration in freshwater ecosystems has been derived by De Vries and colleagues (2011a) by assuming that 10% of these systems are N limited and that 50% of all N is taken up by algae at a C:N ratio of 5.7 and 50% by benthic plants and higher plants at a C:N ratio of 15.7, implying a C–N responses of 1.07 kg C per kg N. However, this value is likely to be an overestimate of the net effect of the additional C fixed by autotrophic organisms, since rapid decomposition in freshwater ecosystems is likely. Consequently, the impact of N inputs on C sequestration in freshwater ecosystems has been neglected in this assessment.

In assessing the N-induced CO2-Cexchange, we multiplied anthropogenic N inputs to these systems (see Table 2) with ranges in C–N responses, according to (De Vries et al., 2011a):

With CO2-Cexchange being the total C exchange (sequestration or release in Tg C yr−1) resulting from N inputs, Ninput,agriculture being the N input by N fertilizer, N manure, N fixation and N deposition in Tg N yr−1 and C–N being the C response to N inputs (kg C per kg N). Results are presented in Table 4.

Table 4. Estimated impacts of anthropogenic N inputs in agriculture, forests, other land, and marine systems for the year 2000 on the global-scale carbon sequestration by multiplying inputs with average C–N responses.

Notes:
(1)
N inputs are based on Bouwman et al. (2013b) for the year 2000 and include inputs from N fertilizer, manure, N fixation, and N deposition. Note that the manure input differs from the value in Table 1 as it excludes 10 TgN yr−1 due to NH3-N emission from housing systems.

(2)
All data on N deposition for terrestrial ecosystems are based on an overlay of the GLC 2000 and the total deposition of NH3 and NOx at 1 x 1 degree based on Dentener et al. (2006a) for the year 2000.

(3)
Based on Liu and Greaver (2009). The range is based on two times the standard deviation presented by these authors.

(5)
Based on Duce et al. (2008). The range is assumed in view of the uncertainty in the percentage of oceanic surface waters that are potentially seasonally N limited, being an important factor in the derived average factor of 4.0 kg C/kg N.

C–N response ratios for agricultural and semi-natural systems were obtained from Liu and Greaver (2009), while C–N response ratios for marine systems were based on Duce et al. (2008). Assuming that in N-limited oceanic waters all N inputs from atmospheric deposition is ultimately taken up in unicellular algae with a Redfield C:N mass ratio of 5.7 and that 70% of oceanic surface waters are N limited (Duce et al., 2008), N-induced C sequestration was estimated at 4.0 kg C per kg N for marine systems (see also De Vries et al., 2011a).

C–N response ratios for three main forest biomes (boreal, temperate, and tropical) were obtained by stoichiometric scaling, based estimates for N retention fractions, N allocation fractions, and C:N rations obtained by a recent literature review by De Vries and colleagues (2014). The forest C sink is mainly driven by an accumulation of C in woody biomass with long turnover times and an increase of organic C in the humus layer litter and mineral soil due to non-woody biomass inputs. The C–N response of woody biomass was estimated at 26.3, 17.5, and 6.1 kg C per kg N for boreal, temperate, and tropical forests, respectively. N-induced C sequestration in soils due to inputs from non-woody biomass production was estimated at 13.5, 13.7, and 5.4 kg C per kg N in global boreal, temperate, and tropical forests, respectively. The overall C–N response at ecosystem scale was thus estimated at 39.8 kg C per kg N in boreal forest, 31.2 kg C per kg N in temperate forest, and 11.5 kg C per kg N in tropical forest (see Table 4).

(2)
Total deposition on temperate and boreal regions of the Northern Hemisphere is estimated at 25 Tg N yr−1, but only half of this is assumed to be deposited on N limited systems (and thus induce C sequestration).

(3)
Article provides estimate for “N carried to oceans by rivers” = 21 Tg N yr−1, and “net N deposition on land” = 13 Tg N yr−1. From this we inferred that they assume that 13/(13+21)= 38% of total N deposition to land is retained.

(5)
Provided value is for 1985. C–N response (and thus total annual N-induced C sequestration) varies over time in the model, as the proportional contribution of different ecosystem C pools with different C:N ratios to total N-induced C storage varies over time.

(6)
Only consider NOx emission from fossil fuel.

(7)
In one model setup the N losses per grid cell are scaled to total N deposition to the grid cell (20% losses at lowest deposition value to 100% at highest deposition value).

(8)
Estimate refers to total deposition (NHx+NOy) on natural ecosystems. Total deposition on land = 22.5 Tg NH3 + 15.4–27.2 Tg NOy; of which on natural ecosystems: 10.1 Tg NHx and 7.7–13.8 Tg NOy, of which on forests: 2.9 Tg NHx and 2.2–4.0 Tg NOy.

The combined results for agricultural (0.13 Pg C y−1) and marine systems (0.18 Pg C y−1) are comparable to those for forests, but the uncertainty here is likely higher than the suggested range in Table 4. In agricultural systems a very high N input is multiplied with a very low and uncertain C–N response. The assumption that arable lands are sequestering C in response to N while grasslands are not is questionable; however, the C–N responses presented by Liu and Greaver (2009) are the only available estimates. Most likely, global agriculture is a very small C sink, considering that global forests contribute more than 90% of the terrestrial C sink (Pan et al., 2011). It is also very challenging to estimate the long-term marine C sink due to N deposition based on the Redfield C:N ratio and the N deposition value, as large uncertainty remains in the allocation of newly fixed C to pathways of biological pump and microbial C pump. In addition, a decrease in BNF resulting from N deposition (Voss et al., 2013) may partially offset the ongoing increase in levels of N inputs and other nutrients, especially Fe and P, may become more limiting and also diminish the stimulating effects of increased N deposition (Okin et al., 2011).

Both O3 exposure experiments and field monitoring have shown that O3 exposure reduces photosynthetic rates and biomass production of many tree species (Karnosky & Pregitzer, 2006; Paoletti, 2005; Wittig et al., 2007). Modeling studies in the United States indicated that tropospheric O3 reduced NPP, with the largest reductions occurring in regions with moderate to high O3 levels as well as agricultural lands due to the high dependence of O3 on stomatal conductance (Felzer et al., 2004, 2005). In another modeling study, Ollinger, Aber, and Reich (1997) found that O3 reduced NPP by 3 to 16% on average, with the lowest reduction occurring in drier areas as a result of the lower stomatal conductance. C sequestration in forest ecosystems is most strongly reduced in O3 “hotspots” that primarily occur in Northern mid-latitudes where the United States, Europe, and China are located (Felzer et al., 2005; Reilly et al., 2007).

An assessment of the impacts of O3 on global C sequestration can be derived by multiplying the spatial variation of O3 exposure (see the section on Global Scale Ozone Exposure Estimates) with the C response to O3 exposure (C–O3 response). Until approximately 2000, most O3 research focused impacts of the external O3 exposure (expressed in ppb) on forest ecosystems, using either an average or peak O3 concentrations approach or a threshold-based approach such as AOT40 (the accumulated O3 exposure over a threshold of 40 ppb during the growing season expressed in ppm. hour) (Ashmore & Fuhrer, 2000). There is consensus that the so-called Phytotoxic Ozone Dose (i.e., the accumulated stomatal flux of O3 above a flux threshold of Y, PODY; expressed in mmol m−2) is a better indicator of harmful O3 exposure than the AOT40 (Ashmore et al., 2004; Emberson, Ashmore, Cambridge, Simpson, & Tuovinen, 2000; Grünhage et al., 2012; Karlsson et al., 2003; Matyssek et al., 2007a; Mills et al., 2010; Simpson, Tuovinen, Emberson, & Ashmore, 2003). While AOT40 is an O3 exposure index based on ambient O3 concentrations, the POD provides a species-specific estimate of the amount (flux) of O3 entering through the stomata. The POD approach takes into account the varying influences of air temperature, water vapor pressure deficit (VPD) of the surrounding leaves, light (irradiance), soil water potential (SWP) or plant available water (PAW), O3 concentration and plant development (phenology) on the stomatal flux of O3.

Below, research approaches are presented that have been used to quantify impacts of O3 on productivity and C sequestration, followed by a simple approach to assess the impacts of O3 on global-scale C sequestration.

Approaches to Quantify Impacts of Ozone on Forest Productivity and Forest Carbon Sequestration

The growth response and related C sequestration of terrestrial ecosystems (most important in this context are forest ecosystems) to O3 is determined by the O3 exposure and the C–O3 response. There are three potential approaches to assess C–O3 responses: (i) experimental O3 addition studies, (ii) field-based monitoring studies across O3 gradients, and (iii) global C–N–O3 modeling approaches. Advantages and limitations of each approach are shortly described below.

Experimental Ozone Exposure Studies

Many experiments have been carried out to assess how much exposure to O3 actually affects tree growth (Chappelka & Samuelson, 1998; Dizengremel, 2001; Ferretti et al., 2002; Karlsson et al., 2003, 2004; Samuelson & Kelly, 2001; Sandermann, Wellburn, & Health, 1997). Experimental results show a clear growth decline in response to elevated O3 exposure, though responses vary between different tree species. A meta-analysis by Wittig et al. (2009), including 313 articles published between 1970 and 2006, assessed how current ambient O3 concentrations decrease productivity relative to preindustrial O3 concentrations. They found that current ambient O3 concentrations of 40 ppb on average significantly reduced total biomass (and therefore C storage) of young trees by 7% compared to trees grown under preindustrial O3 levels near 10 ppb. This value is in the range of 0–10% that was found in a review on ambient O3 effects on forest trees of the Eastern United States (Chappelka & Samuelson, 1998). On average, these results suggest a total biomass decline of 0.23% per ppb change in O3 concentration.

Quantitative data on tree growth responses to O3 exposure have been assessed with respect to both AOT40 (e.g., Karlsson, 2012; Karlsson et al., 2006), and POD1 (e.g., Harmens et al., 2010; Karlsson et al., 2003), mainly based on results from experiments with seedlings under controlled conditions. For example, Karlsson (2012) derived relative changes in stem volume increment rates of 0.26% and 0.49% per unit change in AOT40 for young coniferous and broadleaved trees, respectively. The dose-response relationships were assumed to hold also for trees in the productive age, up to 60 years, but for older trees, the relative changes were assumed to be 50% lower.

Until recently, relative changes in tree growth in response to POD1 were limited to responses on total (woody and non-woody) biomass or NPP (e.g., Calatayud et al., 2011; Harmens et al., 2010). Recently, however, results have also been derived for impacts on net annual increment (e.g., Emberson, 2015), which is an indicator for changes in C sequestration in tree woody biomass with long turnover times. Reported relative changes in tree growth (in %) per unit change in POD1 in terms of NPP and net annual increment (NAI) for beech/birch, oak, Norway spruce/Scots pine, and Holm oak/Aleppo pine are given in Table 6. The various percentages are all based on linear relations between relative growth and POD1 and the R2 present the goodness of fit of the relationships. Considering total biomass, the results suggest a reduction in growth rate of 0.24% and 0.34% per unit change in POD1 for the most common forest species, that is, oak, Norway spruce, and Scots pine.

Table 6. Relative changes in tree growth (in %) per unit change in POD1 (in mmol m−2) in terms of (i) total biomass (left; data based on Harmens et al. (2010) for all tree species except oak and on Calatayud et al. (2011) for oak), and (ii) net annual increment (right; data based on Emberson, 2015).

Tree species

Total biomass

Net annual increment

a2 (POD1)

R2

a2 (POD1)

R2

Beech/Birch

1.10

0.64

1.01

0.63

Oak

0.341

0.67

0.57

0.76

Norway Spruce/Scots Pine

0.24

0.55

0.78

0.77

Holm oak/Aleppo pine

1 Refers to POD1.6

0.063

0.20

0.51

0.66

Field-based monitoring studies across ozone gradients

The impact of O3 on forest ecosystems has also been assessed in field monitoring studies. For example, Ferretti and colleagues (2012a) used data collected in the period 2005–2009 at 16 forest monitoring plots in Trentino, Italy, to investigate the relation between tree growth (reported as Basal Area Increment (BAI)) and site and environmental factors using multiple regression. Their results showed that O3 was not a significant predictor of BAI. These results were further confirmed for the 2000–2009 period, taking into account AOT40 and stomatal flux at the Level II plots (Ferretti et al., 2012b). Braun, Schindler, Rihm, and Flückiger (2007) applied an epidemiological analysis across 83 beech and 61 Norway spruce plots across Switzerland and associated a 7.4% reduction in shoot length with an AOT40 level of 0–10 ppm h for beech, whereas shoot growth of Norway spruce was not affected by O3. Karlsson and colleagues (2006) used relative annual BAI of mature Norway spruce trees in South-Central Sweden during 9 years and found a decline in BAI of 4.6% in 19- to 35-year-old Norway spruce trees that could be correlated with differences in ambient O3 exposures between years in the range of 2–9 ppm h.

The limited results of correlative studies based on monitoring results do not yet confirm the hypothesis that ambient O3 is a key factor explaining spatial and temporal changes of tree crown defoliation and/or forest growth. The problem with routine forest monitoring is, however, that tree growth is a nonspecific response indicator, which is subject to many other stressors than O3 (e.g., Percy & Ferretti, 2004). Furthermore, NOx deposition and O3 exposure are generally correlated, while severe drought conditions may limit O3 uptake by plants, therefore preventing the development of visible injury (Fischer et al., 2005). Consequently, field evidence of O3 effects on forests is limited and extrapolation of experimental results to the real forest condition is problematic (Ferretti et al., 2007).

Global carbon-nitrogen-ozone modeling approaches

Despite the fact that impacts of O3 on the vitality and growth of plants, and especially forests, being received considerable research attention in the last decades, the resulting effects on global C sequestration have only gathered attention rather recently. Sitch et al. (2007) modified the global land carbon cycle model MOSES-TRIFFID by including the effect of O3 on photosynthesis by a factor that accounts for plant O3 uptake and plant-specific sensitivities to O3 uptake, while accounting for interactions between O3 and CO2 on the land-carbon sink through stomatal closure. In the study, O3 response data for European and North American species were extrapolated to represent all global vegetation types. More recently, Sitch et al. (2012) used the model JULES and the MOSES-TRIFFID land surface scheme to assess the effect of O3 exposure on global C sequestration. The interacting effects of CO2, temperature, N, and O3 have been included in a relatively simple way in the process-based biogeochemistry model TEM that simulates the cycling of C, N, and water between vegetation (Felzer et al., 2004). Currently, various global land C cycle models, such as OCN (Zaehle & Friend, 2010) and CLM (Randerson et al., 2009) are working on including the interacting effects of CO2, temperature, N, and O3 simultaneously, but results of simulations are not yet available.

Global-Scale Estimates of Carbon Exchange in Response to Ozone Exposure

The impact of O3 exposure on global-scale CO2 exchange can in principle be quantified by multiplying an O3 exposure measure (AOT40 or POD1) with ranges in C–O3 responses. Karlsson (2012) used this approach to assess O3 impacts on living biomass C stock changes in 10 countries (Sweden, Finland, Norway, Denmark, Estonia, Latvia, Lithuania, Poland, Czech Republic, and Germany). He multiplied nationwide mean values for annual daylight AOT40 with relative changes in stem volume increment rates of 0.26% and 0.49% per unit change in AOT40. By doing so, he estimated that O3 exposure in the year 2000 reduced C storage in living tree biomass in Northern Europe by 10% on average as compared to preindustrial atmospheric O3 levels. Büker and colleagues (2012) used a more sophisticated approach by multiplying modeled POD1 values (estimated at 50 km x 50 km resolution based on O3 and meteorological data provided by EMEP for the year 2000) with relative changes in total biomass per unit change in POD1 as reported in Table 6. In this way, they estimated a reduction of 12% in total C storage in the living biomass of trees in Europe due to O3 exposure in 2000 as compared to preindustrial atmospheric O3 levels. Both estimates from Karlsson (2012) and Büker et al. (2012) are in line with an earlier estimate for large-scale tree yield losses attributable to O3 pollution of 10% for Europe (Broadmeadow, 1998).

At the global scale, estimates of POD1 are not yet available and the approach used by Büker et al. (2012) cannot be applied. Results of the modeling exercise by Sitch et al. (2007) suggested that over the period 1900–2100, O3 exposure reduced land carbon storage by 8.5–16.6% while the reduction in vegetation (mainly) tree C storage was 6.5–19.1%. The range was based on a “high” and “low” sensitive parameterization for each plant functional type to represent species sensitive (broadleaved trees) and less sensitive (conifers) to O3 effects. Applying O3 stomatal flux response relationships in JULES (Sitch et al., 2012), the model predicted that the reduction in C stored in vegetation is 6.2% globally and near 4% in Europe in 2000 compared to 1900.

Based on the above results and the meta-analysis by Wittig et al. (2009), the global-scale impact of O3 exposure on forest growth reduction has been estimated at 10% (5–15%). The total C sink in global established forests has been estimated at 2.30 ±0.49 Pg C yr−1 from an inventory-based global assessment (Pan et al., 2011). A 10% (5–15%) reduction implies a loss in C sink of 0.23 (0.115–0.345) Pg C yr−1.

Discussion and Outlook

Impacts of Nitrogen Fixation on Greenhouse Gas Emissions, Their Uncertainties, and Potential for Improvement

As discussed in detail above, impacts of global-scale human N fixation on net greenhouse gas emissions are determined by uncertainties in (i) drivers, that is, estimated N fixation, N deposition, and NOx-induced O3 exposure fluxes; and (ii) responses, that is, N2O–N, C–N, and C–O3 response functions. The various estimates in drivers and response functions, their uncertainties, and approaches for improving estimates are summarized below.

Drivers of change

Global human nitrogen fixation

The fixation terms of the global reactive N budget, related to fertilizer production (reduced N components), BNF, and fossil fuel production (NOx) are better known than the loss terms. For the year 2010, the total global estimate is 210 Tg N yr−1, divided in 120 Tg N yr−1 for chemical N fixation of which near 100 Tg N yr−1 is fertilizer, 60 Tg N yr−1 for BNF and 30 Tg N yr−1 for fossil fuel NOx emissions (Fowler et al., 2013). Uncertainties in fertilizer are mainly determined by FAO data on fertilizer production, which are likely within 10% at the global scale. Uncertainties in BNF can be as large as 50%, due to uncertainties in the area of legumes and the assessment of N fixation for a leguminous crop. Anthropogenic NOx emissions are relatively well documented, and their uncertainty is partly constrained by observations of NOx, implying that their uncertainty at the global scale is most likely within 25%.

Global-scale N2O emissions are, however, also influenced by recycled N, of which N manure inputs, largely induced by N fertilizer inputs, are by far most important. Global-scale N manure inputs are estimated near 100 Tg N yr−1, being comparable to N fertilizer inputs, but this number is less reliable (most likely up to 25% at the global scale) due to uncertainties in FAO data on livestock numbers, N excretion rates, and N manure use in agriculture. In countries like China and India, animal manure is often discharged directly, affecting the N input to soils but even stronger than the N runoff to surface waters.

Global anthropogenic N deposition

Overall, the global anthropogenic N deposition is determined by global anthropogenic N (NH3 and NOx) emission estimates, both being near 100 Tg N yr−1 and quite evenly distributed over agricultural systems, other terrestrial systems, and marine systems. Anthropogenic NOx emissions and their trends are relatively well documented, but anthropogenic NH3 emissions are less accurately known, especially in regions, with less observations and incomplete information on agricultural activities and practices. Large uncertainties still exist in the estimates of NOx and NH3 emissions from biomass burning and soils, and the understanding of their specific role in cycling of reactive N through ecosystems. The former will benefit from continuously improving satellite information on areas of land burnt, combined with ecosystem models that estimate N content as well as physical information regarding flaming conditions of these fires, which are determining NOx and NH3 emissions. New satellite based observations of NH3 columns from, for example, the IASI and TES instruments, provide new opportunities to more accurately determine NH3 emissions (Clarisse, Clerbaux, Dentener, Hurtmans, & Coheur, 2009; Shephard et al., 2011), and obtain insights in the importance of emission processes, such as bi-directional exchange (Zhu et al., 2015b).

While estimates of wet N deposition are relatively well constrained by observations in North America and Europe, observations are scarce in other parts of the world, including Asia, Africa, and South America. Here, emerging deposition monitoring networks, like EANET in East and Southeast Asia and DEBITS in Africa, need to be expanded. In China, emerging observational datasets on wet/bulk N deposition have led to recent assessments at the national scale (Du et al., 2014; Liu et al., 2013; Xu et al., 2015; Zhu et al., 2015a) and thus may help to constrain modeling results (e.g., Lu & Tian, 2014). Dry deposition estimates are available for North America (i.e., surface gas observations in combination with dry deposition velocities interfered from local meteorology), but elsewhere such analysis is not available or incomplete (Dentener et al., 2014). For instance, Xu and colleagues (2015) have recently estimated dry N deposition in China based on an inferential method using measured concentrations of N-containing gases; however, their results are highly uncertain as they used model-inferred deposition velocities. Therefore, continuous efforts to collect and publish dry deposition estimates are needed.

Global anthropogenic ozone exposure

Outside of Europe and North America, observations of O3 are scarce, limiting the evaluation possibilities for regional and global model simulations of surface and tropospheric column O3 in other regions. Although satellite measurements start providing observations of tropospheric column O3, they do not yet provide much information on surface O3. Therefore regional and global photochemical models will remain the main information source for the next decade or so. These models are generally fairly simplified in their description of land-cover and vegetation dynamics, which are driving the dry deposition models. Hardacre and colleagues (2015) compared results of several global models with measurements at a variety of locations in Europe and North America. They noted differences in estimates of up to a factor of two but found no clear systematic bias over the sites. Better constraints on dry deposition velocities, particularly to grasslands and tropical forests and oceans, should improve model estimates. More consistent and up-to-date land-cover information should be at the basis of these models.

Response functions

Nitrous oxide to nitrogen response functions

Global-scale N2O soil emission factors are estimated near 1.5% for manure N and 1.0% for fertilizer N application. Emission factors for integrated indirect N2O emission downwind and downstream of the site of application are estimated near 1%; however, these factors are more uncertain. Estimates of indirect N2O emissions from oceans are especially uncertain, and many global N2O emission estimates do not even include this source category.

Soil emissions: Global anthropogenic N2O emissions are dominated by emissions from agricultural soils, which are the result of (mainly microbial) production and consumption processes occurring simultaneously within the soil matrix. The understanding how organic and inorganic N fertilizer additions to agricultural soils and atmospheric N deposition to natural ecosystems affect magnitudes and temporal variability of soil N2O emissions is still hampered by the scarcity of experimental studies. Constraining emission magnitudes and variability still requires significant progress in process understanding. New techniques such as laser spectroscopy and isotope techniques offer higher measuring sensitivity and thereby new insights and a better differentiation of source (and sink) processes (e.g., Yamamoto, Uchida, Aklyama, & Nakajima, 2014).

There is still a lack of insight in N2:N2O:NO product ratios during denitrification and how those might vary in response to environmental changes (Butterbach-Bahl & Dannenmann, 2012), and in the importance of microbial diversity and its dynamics in space and time for soil N2O emissions. This lack of understanding makes it difficult to predict how anthropogenic perturbations of the N cycle affect soil N2O emissions both at the global scale and at regional and site scales. Further progress in understanding spatial, and more importantly, temporal changes in soil N2O emissions requires an integrated and coordinated research approach, with targeted process studies on microbial controls of N2O production and consumption—with the latter still hardly being investigated—that are accompanied by long-term field measurements (Luo et al., 2012). Upscaling of measured N2O emission rates is especially problematic. Therefore progress needs to be made putting more observational constraints on estimates of N2O emissions and N balances at farm to regional scales, including better estimates for denitrification N2 losses, with global budgets testing the consistency of the regional estimates. Focusing on the N cycle only, however, is not an advisable long-term strategy, since the N cycle is closely linked to the cycles of C and phosphorus and the availability of other micro-nutrients. Thus, a holistic approach is needed, also including the translation of knowledge in process-based models and adequate testing of these models with observations at various spatio-temporal scales.

Oceanic emissions: Indirect emissions from marine systems are mainly determined by the global fate of N losses and N2O emission factors from these systems. Emission factors are highly uncertain even though values have been reduced strongly going from the 1996 to the 2006 IPCC guidelines, based on more recent insights. Most uncertain are the N2O emissions from deep oceans, which occur naturally (Nevison, Lueker, & Weiss, 2004), but are also enhanced by human- induced N deposition (Duce et al., 2008). Syakila and Kroeze (2011) estimated natural ocean emissions at 3.5 Tg N2O-N yr−1, and anthropogenic N deposition-induced ocean emissions at 1 Tg N2O-N yr−1. Duce and colleagues (2008) derived an even higher value of 1.5 Tg N2O-N yr−1, assuming that 3% of anthropogenic N deposition to oceans (estimated at 54 Tg N yr−1) is emitted as N2O. The value of 3% was derived by dividing a mean global ocean N2O emission of 5.0 Tg N2O-N yr−1, based on the study by Nevison et al. (2004), by an estimated N input to oceans from BNF of 100 Tg N yr−1 and from atmospheric deposition of 67 Tg N yr−1. The atmospheric N input is, however, likely overestimated since Duce et al. (2008) added 30% of organic N inputs, which is likely an overestimate. Actual anthropogenic N deposition on oceans is more likely around 38 Tg N yr−1, which (using the emission factor of 3%) would lead to an estimate for oceanic N2O emissions near 1.0 Tg N2O-N yr−1, similar to that derived by Syakila and Kroeze (2011). Also here, continued experimental studies and “field studies” steering improvement of ecosystem models are the avenues to improve emission estimates.

Carbon to nitrogen response functions

The global impact of human N fixation and associated atmospheric N deposition on terrestrial ecosystem C sequestration is predominantly determined by the responses of the forests C sink to N inputs. The overall average ecosystems (forest and soil) C–N response is estimated near 40, 30, and 10 kg C per kg N for boreal, temperate, and tropical forests, respectively. However, the uncertainty in these numbers is at least 50%. Major uncertainties are the impacts of N input levels and their interaction with other drivers, especially changes in climate and CO2 concentrations, on the C–N response, as discussed below.

Changes in C–N response due to changes in N deposition levels: Changes in the forest C sink response to N input levels are determined by ecosystem N retention, which affects the responses of NPP and heterotrophic respiration (De Vries et al., 2014). In this assessment, N retention fractions, N allocation fractions, and C:N ratios in forest ecosystem compartments were assumed to be constant. However, all these factors likely vary with N deposition level. Nitrogen retention factors most likely show a nonlinear response to N deposition, with several thresholds (De Vries et al., 2014). The N retention fraction in forests stays high at low N deposition until it starts to decrease at intermediate rates of N deposition, followed by a more substantial decrease when N sinks in soil and plant become saturated and finally N retention becomes negligible when complete ecosystem N saturation occurs. (Note that “complete N saturation” in this context refers to what Lovett and Goodale (2011) define as “capacity N saturation,” that is, a state in which the ecosystem does not retain any additional N. “Kinetic N saturation,” on the other hand, is defined as a state in which N leaching starts to increase but N retention still takes place simultaneously (Aber et al., 1989, 1998; Lovett & Goodale, 2011). An increase in leaching can thus be observed long before complete N saturation occurs.) NPP and heterotrophic respiration are proposed to show a similar nonlinear response to increasing levels of N deposition. Reduced values for N retention and also for NPP at high levels of N input are likely caused by soil acidification, nutrient imbalances, and increased occurrence of pests and diseases (see above).

Combined effects of N deposition and climate change on C–N responses: Elevated N deposition occurs simultaneously with other environmental changes, such as increasing atmospheric CO2 concentrations, increasing temperatures, and changing precipitation patterns. There is ample evidence for interactions between these drivers of C sequestration. Interactions can be either synergistic or antagonistic, that is, causing effects that are either larger or smaller than the sum of the effects of single drivers (Zavaleta, Shaw, Chiariello, Mooney, & Field, 2003). For example, increased atmospheric CO2 has a limited effect on NPP in N-limited systems (Reich et al., 2006), but leads to increased N uptake (Finzi et al., 2007) and water use efficiency through reductions in stomatal conductance (Drake & Gonzàlez-Meler, 1997) or by an increase in fine root biomass and increased ectomycorrhizal activity (Luo et al., 2004). Increased temperature often results in increased soil mineralization and thus higher N availability, which will enhance NPP and C sequestration (NEP) especially when N is limiting (Bazzaz & Sombroek, 1996). However, increased N mineralization will eventually result in N limitation or substrate depletion (Felzer et al., 2004; Luo & Zhou, 2006). These interactions with CO2 and climate complicate the assessment of future impacts of N deposition on the forest C sink. These challenges call for improved understanding of these interactions and their mechanisms, as a basis for implementation in process-based models that allow upscaling of these interacting impacts. This understanding can be obtained via long-term multifactorial experiments with a standardized methodology accounting for multilevel global change factors (Kardol, De Long, & Sundqvist, 2012), focusing specifically on the C sequestration response.

Carbon to ozone exposure response functions

Results from experimental tree growth (total biomass) response studies to O3 exposure indicate an overall average reduction in total biomass growth rate near 0.25% (with a variation of more than 50%) per unit change in either O3 concentration (ppb), AOT40 (ppm. hour) or POD1 (mmol m−2), but uncertainties are very large as discussed below.

Differences in C–O3 responses in experimental studies and under field conditions: Unfortunately, available knowledge about O3 effects on trees has mainly been gained from experimental studies conducted on young (juvenile) trees in greenhouses, growth chambers, or open-top chambers, whereas experimental data sets describing O3 effects on adult trees are scarce (De Vries et al., 2014). While results from experimental studies under controlled conditions provide insights into the mechanisms through which O3 affects forest trees, it is difficult to upscale these findings to adult trees grown under field conditions (Baumgarten et al., 2000; Kolb, Fredericksen, Steiner, & Skelly, 1997; Schaub et al., 2005). The few experimental O3 exposure studies with large mature trees under field conditions indicate that much of the available information about O3 effects on trees does not apply well to actual forest conditions and large trees (Matyssek & Sandermann, 2003) and the extrapolation of results obtained from seedlings to mature trees under real forest condition has been severely challenged (Kolb & Matyssek, 2003). However, Karlsson (2012) concluded that there is no indication so far that mature trees are less affected by elevated O3 concentrations than juvenile trees. Moreover, Wittig and colleagues (2009) even suggested that chamber studies on young trees might underestimate O3 impacts compared to open-air field studies over longer periods. There are, however, no publications yet relating forest growth changes to changes in the POD1 based on results from field gradient studies or long-term field monitoring studies. Such monitoring results are highly relevant in view of the debate described above.

Combined effects of N deposition and climate change on C–O3 responses: As with C–N responses, the impact of O3 exposure on growth and C sequestration (C–O3 response) is affected by climate, CO2 concentration, and N deposition. Increased water stress and CO2 levels can lead to stomatal closure, which reduces the uptake of O3, thus limiting the damaging effect of O3 on photosynthesis (Karnosky et al., 2003). Elevated temperature leads to stomatal opening, thus increasing the effect of CO2 and O3 (Mauzerall & Wang, 2001). Finally, O3 exposure impacts may be lower at optimal N availability than at excessive/toxic or suboptimal N availability, as shown by Pell, Sinn, and Johansen (1995).

Spatial variability

The reliability of the assessment of impacts of global-scale human N fixation on net greenhouse gas emissions is also affected by the spatial variability in estimated N inputs, N deposition, and NOx-induced O3 exposure and in the N2O–N, C–N and C–O3 response functions. Global human-induced N inputs to agricultural ecosystems through N fertilizer and manure application are highly unevenly distributed. In many developing countries, N inputs are lower than crop N uptake (thus mining the soil), while many developed countries and rapidly growing economies experience excessive N surpluses (Vitousek et al., 2009). Nutrient-limited regions include much of Africa (Liu et al., 2010; Sanchez, 2002; Stoorvogel, Smaling, & Janssen, 1993) as well as large areas of Latin America and Southeast Asia (MacDonald, Bennett, Potter, & Ramankutty, 2011). In those areas, an increase in N input is needed to avoid land degradation and increase crop yields, while in large parts of China, the United States, and Europe N application in excess of crop demand causes elevated N losses to air and water (Sutton et al., 2013). In China, there are even areas where N application can be reduced while enhancing yields and reducing environmental impacts (Ju et al., 2009).

Apart from regional variation in the “drivers” of the N-cycle, also the impacts per kg of reactive N differ largely in space. This is only partly included in the assessment, for example, by using a lower C–N response for tropical forests as these forests are generally more P limited than N limited. The impact of N deposition on CO2 uptake is thus likely to be stronger at mid-latitudes.

Spatial variations also affect processes influencing radiative forcing, not specifically dealt with in this article. For instance, the impact of N2O emission on radiative forcing is larger in the tropics than at mid-latitudes, and there are large differences in atmospheric responses to NOx and NH3 emissions, both for aerosol formation and its associated feedbacks and radiative impacts, as well its influences on O3 and CH4 (Shindell et al., 2009).

Impacts on greenhouse gas emissions

The overall impact of human N fixation on on greenhouse emissions is determined by the effects of (i) N inputs on increased N2O emissions, (ii) N inputs on increased CO2 uptake, and (iii) NOx-induced O3 exposure on reduced CO2 uptake, as discussed in the previous sections. To make an overall assessment, the global-scale contribution of NOx emissions to O3 exposure has to be estimated and N2O emissions have to be expressed in CO2 equivalents (CO2 eq).

The contribution of NOx to the O3 burden varies in space and time since the O3 production rate depends nonlinearly on the location (e.g., tropics vs mid-latitudes), the magnitude and ratio of the concentrations of NOx and volatile organic compounds (VOC), including CH4. Overall, the relative benefit of reducing NOx emissions for O3 mitigation is higher than the benefit of decreasing VOC emissions. Recent model evaluations (Stevenson et al., 2006; Young et al., 2013) find near-linear relationships between O3 burden and NOx emissions for preindustrial, present, and future scenarios. However, in these studies changes in NOx emissions are strongly co-varying with emissions of CO, VOC, and CH4, and hence O3 changes cannot be attributed to NOx per se. Attribution of O3 changes to NOx emissions is typically done by tagging of NOx emissions or by emission perturbation experiments. Grewe, Dahlmann, Matthes, and Steinbrecht (2012) discuss the differences in these techniques that highlight different aspects of the importance of O3 emission changes. In this study, we use the emission perturbation study by Wang and Jacob (1998), who estimated that the O3 increase from the preindustrial era to 1990 is determined for 60% by increases in NOx, whereas the remaining part is due to increases in emissions of CO, CH4 and non-methane volatile organic compounds (NMVOC). We thus applied a factor of 0.6 to estimate the contribution of human N (NOx) fixation to the global reduction in CO2 uptake induced by O3 pollution. Other techniques could attribute larger fractional contributions to O3 production from NOx.

The climate effect of N2O emissions can be compared to the effect of CO2 emissions by comparing the amount of heat trapped by a certain mass of N2O as compared to a similar mass of CO2, calculated over a specific time interval. Considering a 100-year period and including climatic feedbacks, the most recent available factor to convert N2O to CO2 eq is 298 kg CO2/kg N2O (see Table 8.7 of the Fifth IPCC Assessment Report (Myhre et al., 2013),

Ranges in estimates of the impact of human N fixation on N2O and CO2 emissions, expressed in Tg CO2 eq yr−1, are given in Table 7. It can be concluded that CO2 sequestration most likely largely (on average 75%) compensates N2O emissions, but not completely. Furthermore, it is questionable whether the strength of the CO2 sink will not diminish in the longer term. A review by De Vries et al. (2014) evaluated those long-term impacts by assessing the probability that N deposition will reduce N retention due to decreased soil C:N ratios in ecosystems, causing elevated N leaching and reduced growth responses. Most likely, N effects on forest C sequestration are long-lasting below N deposition levels of 10–15 kg N ha−1 yr−1, a level that is only exceeded in forests near industrialized regions. Moreover, high levels of N deposition may lead to a nutrient imbalance and a shift toward P limitation, possibly diminishing stimulating effects of N deposition on plant growth and CO2 uptake (Du et al., 2016; Peñuelas et al., 2012; Peñuelas et al., 2013).

Table 7. Ranges in N2O emissions and CO2 emission estimates in response to N inputs and O3 exposure and total effect on the emission in Pg CO2-C eq yr−1.

Notes:
(1)
This value was derived by multiplying global N2O-N emissions of 8.0 (7.0–9.0) Tg N2O-N yr−1 (see Table 3.1) with 44/28 (the molar ratio of N2O to N2O-N) with a 100-year global warming potential of 298 and with 12/44 (the molar ratio of CO2-C to CO2).

(3)
This value was derived by multiplying an O3-induced reduced CO2-C emission of 0.115–0.345 Pg C yr−1 with 0.6 (the contribution of NOx to O3 exposure).

Considering the likely impacts of NOx-induced O3 exposure on CO2 emissions (reduced CO2 uptake), the overall impact of human N fixation is most likely a net increase in greenhouse gas emissions (Table 7). However, adverse NOx-induced O3 effects on NPP and thereby on CO2 emissions (warming effect) are uncertain due to the effect of climate on the phytotoxic ozone dose (POD), which is a much better metric for O3 exposure than the average O3 concentration of AOT40.

Impacts on radiative forcing

Despite the large uncertainties (see also the discussion above), this overview indicates that the overall impact of human N fixation is most likely a net increase in greenhouse gas emissions (Table 7). As noted in the introduction, however, N influences the Earth’s radiative forcing in several other ways that are not the focus of this article. NH3 and NOx emissions induce formation of particulate matter (mainly increasing the formation of aerosol NH4NO3 and increasing oxidation of SO2 to form (NH4)2SO4), owing to larger amounts of oxidants. Both components lead to cooling of the atmosphere (Butterbach-Bahl et al., 2011b; Erisman et al., 2011). In addition, O3 is a greenhouse gas, and NOx-induced O3 formation thus leads to warming of the atmosphere. On the other hand, NOx emissions also lead to the production of OH radicals, which enhance the chemical destruction of CH4 and thus shorten the atmospheric lifetime of CH4 (cooling effect). In addition, the lower CH4 concentration leads to a lower O3 production. While the overall impact of NH3 in the atmosphere is cooling, this is less obvious for NOx. A limited amount of studies (e.g., Fuglestvedt et al., 2010; IPCC, 2013; Shindell, 2009) indicate a negative net radiative forcing for NOx emissions due to largely compensating effects of increased O3 production and decreased CH4, and potentially large aerosol cooling. The overall effect of reactive N on radiative forcing is therefore likely to largely compensate the overall warming effect in Table 7, and likely to be cooling, subject to large uncertainty.

Other impacts of nitrogen inputs

This article presents an overview of the global-scale impacts of human N fixation on the climate expressed through the common metric of CO2 equivalent emissions. Based on the most recent scientific insights, we estimate that human reactive N fixation leads to a net increase in greenhouse gas emissions, however, the magnitude of this effect is small when comparing it to total global anthropogenic emissions of CO2, CH4 and N2O (estimated at 11.9 Pg CO2-C eq for the year 2000, EC-JRC & PBL, 2011).

While we focused on the net effect of human N fixation on greenhouse gas emissions at the global scale, it has to be realized that human N fixation has several other impacts, both beneficial (e.g., increasing food production) and detrimental (e.g., reducing biodiversity and human health) (see, e.g., Erisman et al., 2015; Sutton et al., 2013). Environmental and agricultural policies targeted on reducing reactive N emissions thus need to be aware of and balance the cascade of impacts of reactive N. Furthermore, it is important to realize that the drivers of human N fixation as well as its impacts and options to mitigate N emissions are subject to large spatial variations. Currently, most developed countries as well as some emerging economies in Southeast Asia experience high N inputs to agriculture as well as high N emissions from combustion processes, with associated negative environmental and health effects. In many developing regions, on the other hand, human N fixation is currently low; however, these regions are expected to show the largest increases in human N inputs in the future. Better understanding these regional variations will be key to addressing the challenge of maximizing the benefits of reactive N for food and energy production while simultaneously minimizing its many environmental impacts, of which climate change is only one.

IPCC (2007). Climate change 2007: The physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge, U.K.: Cambridge University Press.Find this resource: